Lung Nodule Classification Using Taguchi-Based Convolutional Neural Networks for Computer Tomography Images

Cheng-Jian Lin,Yu-Chi Li

doi:10.3390/electronics9071066

Abstract

Lung cancer occurs in the lungs, trachea, or bronchi. This cancer is often caused by malignant nodules. These cancer cells spread uncontrollably to other organs of the body and pose a threat to life. An accurate assessment of disease severity is critical to determining the optimal treatment approach. In this study, a Taguchi-based convolutional neural network (CNN) was proposed for classifying nodules into malignant or benign. For setting parameters in a CNN, most users adopt trial and error to determine structural parameters. This study used the Taguchi method for selecting preliminary factors. The orthogonal table design is used in the Taguchi method. The final optimal parameter combination was determined, as were the most significant parameters. To verify the proposed method, the lung image database consortium data set from the National Cancer Institute was used for analysis. The database contains a total of 16,471 images, including 11,139 malignant nodule images. The experimental results demonstrated that the proposed method with the optimal parameter combination obtained an accuracy of 99.6%.

Highlights

Lung cancer is the commonest form of cancer with the highest death rate both in developed and developing countries
Into multiple stacked meta-convolutional blocks and fully connected blocks, each of which may contain convolution, pooling, full connection, batch normalization, activation and exit operations to convert the architecture to integer code
An ACL algorithm [18] divided the deep convolutional neural network (DCNN) into multiple stacked meta-convolutional blocks and fully connected blocks, each of which might contain convolution, pooling, full connection, batch normalization, activation and exit operations to convert the architecture to integer code

Summary

Introduction

Lung cancer is the commonest form of cancer with the highest death rate both in developed and developing countries. Female lung cancer is the number one cause of cancer-related death among women in Taiwan. Lung cancer is the leading cause of death in the United States and East Asia [1,2]. The smoking behaviors and environmental factors that cause lung cancer have been extensively studied [3]. Chest x-rays, computed tomography (CT), and magnetic resonance imaging have been used to physically analyze tissues to diagnose lung cancer [4,5]. Systemic therapy for these patients includes chemotherapy or targeted therapy. Detection of lung cancer is critical in reducing misdiagnosis by physicians. CAD assists imaging specialists to determine, identify, and evaluate lung lesions and nodules in digital CT images

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Conclusion